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Self-Training With Noisy Student Improves ImageNet Classification Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 10687-10698). Self-training with Noisy Student improves ImageNet classification The abundance of data on the internet is vast. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. We use a resolution of 800x800 in this experiment. FixMatch-LS: Semi-supervised skin lesion classification with label putting back the student as the teacher. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. on ImageNet ReaL Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. For classes where we have too many images, we take the images with the highest confidence. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. If nothing happens, download Xcode and try again. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. to use Codespaces. Models are available at this https URL. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Please On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. supervised model from 97.9% accuracy to 98.6% accuracy. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. In other words, small changes in the input image can cause large changes to the predictions. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Are you sure you want to create this branch? GitHub - google-research/noisystudent: Code for Noisy Student Training On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. over the JFT dataset to predict a label for each image. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Self-training with Noisy Student improves ImageNet classification and surprising gains on robustness and adversarial benchmarks. The comparison is shown in Table 9. 10687-10698 Abstract However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. Papers With Code is a free resource with all data licensed under. Work fast with our official CLI. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. It can be seen that masks are useful in improving classification performance. Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. unlabeled images. We iterate this process by putting back the student as the teacher. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. We also study the effects of using different amounts of unlabeled data. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. In contrast, the predictions of the model with Noisy Student remain quite stable. Noisy Student leads to significant improvements across all model sizes for EfficientNet. We apply dropout to the final classification layer with a dropout rate of 0.5. Yalniz et al. Code for Noisy Student Training. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Self-Training With Noisy Student Improves ImageNet Classification The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Zoph et al. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Learn more. IEEE Trans. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. . We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Noisy Students performance improves with more unlabeled data. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Agreement NNX16AC86A, Is ADS down? 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting.